The present invention relates to the domain of the sensors and especially to the domain of the use of a sensor while its value(s) may slightly drift.
The approaches described in this section could be pursued, but are not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section. Furthermore, all embodiments are not necessarily intended to solve all or even any of the problems brought forward in this section.
Normally, when using a sensor, the users may expect that the output value may be constant if the detected characteristics are constant/are identical. For instance, for a pressure sensor, if the pressure to be detected is constant at 2 bars, it may be expected that the output value of the sensor is constant (e.g. 128 for 2 bars, 64 for 1 bar, 32 for 0.5 bars, etc.).
Nevertheless, this is not always the case.
Due to some technologies used for some sensors, the output value may slightly drift: the output value (e.g. numerical) may be 128 for 2 bars at a time t0, between 115 and 120 for 2 bars at a time t0+2 h, etc.
Therefore, when it should be determined whether the output value represents a given state (e.g. a “high” value of the sensor may be associated with the state of the presence of a user on a pressure sensor, a “low” value of the sensor may be associated with the state of non-presence of a user on a pressure sensor) it may be difficult to determine in which state the sensor is.
For instance, in the example of FIG. 1, the output value of a pressure sensor is represented by the curve 100. For instance, this pressure sensor is designed to detect if a user is on a bed (state: presence of the user or “high” state) or no in its bed (state: non-presence of the user or “low” state). Even if this example has only two different states, it is possible to generalize this example to any number of states (e.g. a motion sensor which has three states: one state for “no movement”, one state for “walking”, one step for “running”).
At t0, the output value of the pressure may be p0 which is, at that time t0, associated with the low state. Between time t0 and t1, the output value of the sensor may decrease (due for instance to leaks). At time t1, it is apparent, due to the sudden change of the output value up to p1, that a person is present in the bed (high state). One may detect that this is a “high state” as the pressure p1 is greater than p1. Between time t1 and t2, the output value of the sensor may decrease (same reason(s) as above). At time t2, it is apparent, due to the sudden change of the output, that the person has left (low state). Between time t2 and t3, the output value of the sensor may decrease (same reason(s) as above). At time t3, it is apparent, due to the sudden change of the output value up to p3, that a person is present in the bed (high state). Nevertheless, it is noted that p3 is less than p0. Therefore, if the value of p0 is used as a reference of the low state, it may induce incorrect detections/determination of the state.
In addition, even if a human looking at the curve 100 of FIG. 100 may “visually” detect the “sudden change” at time t1, t2 or t3, it may be difficult to algorithmically determine these changes of state, especially if the curve 100 has a lot of noise due to imperfections of the sensors, due to variations of temperature or due to variations of pressure.
Thus, there is a need for accurately determining various states based on a sensor whose output value may drift in time, with possibility noisy conditions.
Most of the time in the prior art, only one state (i.e. a normal state) may be determined. The drifting of the sensor is determined by computing a running average or sliding window average of the value of the sensor. This process assumes that, most of the time, the sensor detects a “normal state”: this is the reason a simple average may provide a satisfactory result in this specific situation. Nevertheless, if a plurality of states may be detected with important probabilities (e.g. three states may be detected with a respective probability rating of 30%, 30% and 40%), it is not possible to perform a simple mean.
In addition, the memory of the device which should perform such determination could be limited. Therefore, the process used may limit the memory used for said determination.